Sewer damage detection from imbalanced CCTV inspection data using deep convolutional neural networks with hierarchical classification

2019/02/13 by Duanshun Li, Anran Cong, Shuai Guo · 3 citations
Engineering · #Concrete Corrosion and Durability #Infrastructure Maintenance and Monitoring #Water Systems and Optimization

paper · doi:10.1016/j.autcon.2019.01.017

openalex publication_date 2019/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Citations

• Assessment technologies for sewer system rehabilitation
• Automated detection of surface defects in water and sewer pipes
• Vision meets robotics: The KITTI dataset
• Learning from Imbalanced Data
• An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
• A systematic study of the class imbalance problem in convolutional neural networks
• Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks
• Deep Residual Learning for Image Recognition

Cited by

• Self-supervised learning for multi-label sewer defect classification
• Sewer pipeline condition assessment and defect detection using computer vision
• A Fuzzy Rule-based Neuro-Symbolic Approach for Pipe Severity Prediction in Sewer Networks